
where \(h_i\) 's are convex and smooth. We focus on the case where the dimension of the problem \(n\)  is high, and the number of constraints \(m\)  is low.
This captures several important ML applications, like multiple kernel learning [1]}, semi-supervised learning [2]}, triangulation in computer vision [3]}, applications in signal processing [4]}, solving constrained MDPs [5]}, [6]}.
